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Federated learning, conducive to solving data privacy and security problems,has attracted increasing attention recently. However, the existing federatedboosting model sequentially builds a decision tree model with the weak baselearner, resulting in redundant boosting steps and high interactivecommunication costs. In contrast, the federated bagging model saves time bybuilding multi-decision trees in parallel, but it suffers from performanceloss. With the aim of obtaining an outstanding performance with less time cost,we propose a novel model in a vertically federated setting termed as FederatedGradient Boosting Forest (FedGBF). FedGBF simultaneously integrates theboosting and bagging's preponderance by building the decision trees in parallelas a base learner for boosting. Subsequent to FedGBF, the problem ofhyperparameters tuning is rising. Then we propose the Dynamic FedGBF, whichdynamically changes each forest's parameters and thus reduces the complexity.Finally, the experiments based on the benchmark datasets demonstrate thesuperiority of our method.

FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging